Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis
Abstract Nasopharyngeal carcinoma (NPC) is the most prevalent type of head- and -neck cancer, and its diagnosis and treatment are currently facing significant challenges. This study aimed to identify biomarkers associated with NPC by performing bioinformatic analysis on the GSE12452, GSE53819, and G...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-03047-4 |
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| author | Cong Fu Lin Sun Lili Zhang Tong Zhou Yanzhi Bi |
| author_facet | Cong Fu Lin Sun Lili Zhang Tong Zhou Yanzhi Bi |
| author_sort | Cong Fu |
| collection | DOAJ |
| description | Abstract Nasopharyngeal carcinoma (NPC) is the most prevalent type of head- and -neck cancer, and its diagnosis and treatment are currently facing significant challenges. This study aimed to identify biomarkers associated with NPC by performing bioinformatic analysis on the GSE12452, GSE53819, and GSE64634 datasets from the GEO database. First, differentially expressed genes (DEGs) between NPC and normal nasopharyngeal tissues were screened. Then, these DEGs were subjected to RobustRank Aggregation analysis. Through Receiver Operating Characteristic (ROC) analysis and three machine-learning models, biomarkers such as DNAH5, ZMYND10, LRRC6, ARMC4, DNAI2, and DNALI1 were identified. Enrichment analysis was performed to uncover the common pathways of these biomarkers. Using the Comparative Toxicogenomics Database (CTD), target drugs for NPC were predicted based on these biomarkers. Additionally, immune infiltration analysis was carried out to study the relationship between these biomarkers and immune cells. A regulatory network was also constructed. It was found that these biomarkers are mainly involved in cytokine–cytokine receptor interaction, and some are part of common cancer-related signaling pathways. In addition, quantitative real time polymerase chain reaction (qRT-PCR) results showed that the expression levels of all biomarkers were significantly elevated in normal cell samples. DNAH5 and ZMYND10 were significantly higher in normal surrounding tissues. These findings provided potential support for the early clinical diagnosis and treatment of nasopharyngeal carcinoma patients. |
| format | Article |
| id | doaj-art-5f144c21096e4f0a9b33b073e793d444 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-5f144c21096e4f0a9b33b073e793d4442025-08-20T03:03:37ZengSpringerDiscover Oncology2730-60112025-07-0116112110.1007/s12672-025-03047-4Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysisCong Fu0Lin Sun1Lili Zhang2Tong Zhou3Yanzhi Bi4Department of Oncology, Changzhou Cancer (Fourth People’s) HospitalDepartment of Oncology, Affiliated Hospital of Soochow UniversityDepartment of Cardiology, Affiliated Hospital of Jiangsu UniversityDepartment of Oncology, Changzhou Cancer (Fourth People’s) HospitalDepartment of Oncology, Changzhou Cancer (Fourth People’s) HospitalAbstract Nasopharyngeal carcinoma (NPC) is the most prevalent type of head- and -neck cancer, and its diagnosis and treatment are currently facing significant challenges. This study aimed to identify biomarkers associated with NPC by performing bioinformatic analysis on the GSE12452, GSE53819, and GSE64634 datasets from the GEO database. First, differentially expressed genes (DEGs) between NPC and normal nasopharyngeal tissues were screened. Then, these DEGs were subjected to RobustRank Aggregation analysis. Through Receiver Operating Characteristic (ROC) analysis and three machine-learning models, biomarkers such as DNAH5, ZMYND10, LRRC6, ARMC4, DNAI2, and DNALI1 were identified. Enrichment analysis was performed to uncover the common pathways of these biomarkers. Using the Comparative Toxicogenomics Database (CTD), target drugs for NPC were predicted based on these biomarkers. Additionally, immune infiltration analysis was carried out to study the relationship between these biomarkers and immune cells. A regulatory network was also constructed. It was found that these biomarkers are mainly involved in cytokine–cytokine receptor interaction, and some are part of common cancer-related signaling pathways. In addition, quantitative real time polymerase chain reaction (qRT-PCR) results showed that the expression levels of all biomarkers were significantly elevated in normal cell samples. DNAH5 and ZMYND10 were significantly higher in normal surrounding tissues. These findings provided potential support for the early clinical diagnosis and treatment of nasopharyngeal carcinoma patients.https://doi.org/10.1007/s12672-025-03047-4NPCRobustRank aggregationBiomarkerMachine learning modelDiagnosis |
| spellingShingle | Cong Fu Lin Sun Lili Zhang Tong Zhou Yanzhi Bi Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis Discover Oncology NPC RobustRank aggregation Biomarker Machine learning model Diagnosis |
| title | Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis |
| title_full | Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis |
| title_fullStr | Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis |
| title_full_unstemmed | Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis |
| title_short | Identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis |
| title_sort | identification of key biomarkers and potential therapeutic drugs in nasopharyngeal carcinoma based on comprehensive bioinformatics analysis |
| topic | NPC RobustRank aggregation Biomarker Machine learning model Diagnosis |
| url | https://doi.org/10.1007/s12672-025-03047-4 |
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